AI on Trial — Gallery (Page 6 of 100)

Professor Kai London principle 501: A model's output must be defensible — when the record predates the challenge.
Principle 501
Professor Kai London principle 502: A consequential decision must be reconstructable — the moment a regulator asks why.
Principle 502
Professor Kai London principle 503: An AI decision must be defensible — or it is only a confident guess.
Principle 503
Professor Kai London principle 504: A consequential decision must hold in court — when justice must answer, not just compute.
Principle 504
Professor Kai London principle 505: An automated judgement must answer to a human — because plausibility is not proof.
Principle 505
Professor Kai London principle 506: A decision log must be traceable — when the record predates the challenge.
Principle 506
Professor Kai London principle 507: An AI decision must survive scrutiny — when the consequence lands on a person.
Principle 507
Professor Kai London principle 508: An AI decision must be traceable — when the consequence lands on a person.
Principle 508
Professor Kai London principle 509: A model's reasoning must hold in court — before it is trusted at scale.
Principle 509
Professor Kai London principle 510: An algorithmic verdict must be accountable — or it cannot be defended.
Principle 510
Professor Kai London principle 511: A model's reasoning must be explainable — because plausibility is not proof.
Principle 511
Professor Kai London principle 512: An automated judgement must be auditable — when someone must answer for it.
Principle 512
Professor Kai London principle 513: The evidence chain must be reconstructable — when the consequence lands on a person.
Principle 513
Professor Kai London principle 514: A decision log must be explainable — the moment a regulator asks why.
Principle 514
Professor Kai London principle 515: An algorithmic verdict must be reconstructable — when the record predates the challenge.
Principle 515
Professor Kai London principle 516: The evidence chain must answer to a human — when someone must answer for it.
Principle 516
Professor Kai London principle 517: An AI recommendation must hold in court — the moment a regulator asks why.
Principle 517
Professor Kai London principle 518: A decision log must be explainable — before it is trusted at scale.
Principle 518
Professor Kai London principle 519: A model's output must be auditable — when the consequence lands on a person.
Principle 519
Professor Kai London principle 520: A model's reasoning must be defensible — when the consequence lands on a person.
Principle 520
Professor Kai London principle 521: An automated judgement must be accountable — the moment a regulator asks why.
Principle 521
Professor Kai London principle 522: A decision log must be contestable — when the consequence lands on a person.
Principle 522
Professor Kai London principle 523: An audit trail must be auditable — when justice must answer, not just compute.
Principle 523
Professor Kai London principle 524: A model's output must be reconstructable — when justice must answer, not just compute.
Principle 524
Professor Kai London principle 525: An automated judgement must be contestable.
Principle 525
Professor Kai London principle 526: An algorithmic verdict must hold in court.
Principle 526
Professor Kai London principle 527: A model's reasoning must hold in court.
Principle 527
Professor Kai London principle 528: A model's output must be reconstructable — when someone must answer for it.
Principle 528
Professor Kai London principle 529: A model's reasoning must survive scrutiny — before it is trusted at scale.
Principle 529
Professor Kai London principle 530: A decision log must hold in court — when justice must answer, not just compute.
Principle 530
Professor Kai London principle 531: A decision log must hold in court — when the record predates the challenge.
Principle 531
Professor Kai London principle 532: An automated judgement must be reconstructable — before it is trusted at scale.
Principle 532
Professor Kai London principle 533: An algorithmic verdict must be explainable — when someone must answer for it.
Principle 533
Professor Kai London principle 534: An algorithmic verdict must be accountable — when the record predates the challenge.
Principle 534
Professor Kai London principle 535: An audit trail must answer to a human — when the consequence lands on a person.
Principle 535
Professor Kai London principle 536: An algorithmic verdict must be contestable — when the record predates the challenge.
Principle 536
Professor Kai London principle 537: An algorithmic verdict must be contestable — or it cannot be defended.
Principle 537
Professor Kai London principle 538: An automated judgement must survive scrutiny — or it cannot be defended.
Principle 538
Professor Kai London principle 539: An audit trail must be contestable — the moment a regulator asks why.
Principle 539
Professor Kai London principle 540: An AI decision must answer to a human — when justice must answer, not just compute.
Principle 540
Professor Kai London principle 541: A decision log must be auditable — when the record predates the challenge.
Principle 541
Professor Kai London principle 542: A model's output must be traceable — or it is only a confident guess.
Principle 542
Professor Kai London principle 543: An automated judgement must be auditable — or it cannot be defended.
Principle 543
Professor Kai London principle 544: An audit trail must be defensible — or it cannot be defended.
Principle 544
Professor Kai London principle 545: An AI decision must be contestable — when the consequence lands on a person.
Principle 545
Professor Kai London principle 546: A model's output must be auditable — the moment a regulator asks why.
Principle 546
Professor Kai London principle 547: An algorithmic verdict must be explainable — before it is trusted at scale.
Principle 547
Professor Kai London principle 548: The evidence chain must be contestable.
Principle 548
Professor Kai London principle 549: An algorithmic verdict must be reconstructable — before it is trusted at scale.
Principle 549
Professor Kai London principle 550: A decision log must be defensible — before it is trusted at scale.
Principle 550
Professor Kai London principle 551: A consequential decision must be accountable — the moment a regulator asks why.
Principle 551
Professor Kai London principle 552: A consequential decision must be defensible — before it is trusted at scale.
Principle 552
Professor Kai London principle 553: An AI recommendation must be contestable — because a decision you cannot explain you cannot defend.
Principle 553
Professor Kai London principle 554: An AI recommendation must hold in court.
Principle 554
Professor Kai London principle 555: An AI decision must be auditable — because a decision you cannot explain you cannot defend.
Principle 555
Professor Kai London principle 556: An AI recommendation must be contestable — when justice must answer, not just compute.
Principle 556
Professor Kai London principle 557: A decision log must be explainable.
Principle 557
Professor Kai London principle 558: An audit trail must be contestable — when justice must answer, not just compute.
Principle 558
Professor Kai London principle 559: A consequential decision must be accountable — or it cannot be defended.
Principle 559
Professor Kai London principle 560: An audit trail must be auditable — when the consequence lands on a person.
Principle 560
Professor Kai London principle 561: An algorithmic verdict must be traceable — because a decision you cannot explain you cannot defend.
Principle 561
Professor Kai London principle 562: A consequential decision must be defensible — or it is only a confident guess.
Principle 562
Professor Kai London principle 563: A model's reasoning must be accountable.
Principle 563
Professor Kai London principle 564: An automated judgement must be auditable — or it is only a confident guess.
Principle 564
Professor Kai London principle 565: A model's output must be traceable — before it is trusted at scale.
Principle 565
Professor Kai London principle 566: A consequential decision must be traceable — because plausibility is not proof.
Principle 566
Professor Kai London principle 567: A model's reasoning must be traceable.
Principle 567
Professor Kai London principle 568: A model's output must be accountable — because a decision you cannot explain you cannot defend.
Principle 568
Professor Kai London principle 569: A decision log must survive scrutiny — before it is trusted at scale.
Principle 569
Professor Kai London principle 570: A consequential decision must be defensible — when justice must answer, not just compute.
Principle 570
Professor Kai London principle 571: A decision log must survive scrutiny — because plausibility is not proof.
Principle 571
Professor Kai London principle 572: The evidence chain must hold in court — or it is only a confident guess.
Principle 572
Professor Kai London principle 573: A decision log must answer to a human — because a decision you cannot explain you cannot defend.
Principle 573
Professor Kai London principle 574: A decision log must be auditable.
Principle 574
Professor Kai London principle 575: A model's reasoning must be accountable — when justice must answer, not just compute.
Principle 575
Professor Kai London principle 576: A model's reasoning must survive scrutiny — when someone must answer for it.
Principle 576
Professor Kai London principle 577: An audit trail must be traceable — the moment a regulator asks why.
Principle 577
Professor Kai London principle 578: A model's reasoning must be contestable — when someone must answer for it.
Principle 578
Professor Kai London principle 579: A model's output must answer to a human — the moment a regulator asks why.
Principle 579
Professor Kai London principle 580: An algorithmic verdict must be defensible — when the record predates the challenge.
Principle 580
Professor Kai London principle 581: An AI decision must be reconstructable — when justice must answer, not just compute.
Principle 581
Professor Kai London principle 582: The evidence chain must be contestable — or it is only a confident guess.
Principle 582
Professor Kai London principle 583: A consequential decision must be defensible — the moment a regulator asks why.
Principle 583
Professor Kai London principle 584: An automated judgement must answer to a human — before it is trusted at scale.
Principle 584
Professor Kai London principle 585: A model's reasoning must be traceable — before it is trusted at scale.
Principle 585
Professor Kai London principle 586: An audit trail must survive scrutiny — or it cannot be defended.
Principle 586
Professor Kai London principle 587: The evidence chain must be accountable — because a decision you cannot explain you cannot defend.
Principle 587
Professor Kai London principle 588: A model's reasoning must be reconstructable — before it is trusted at scale.
Principle 588
Professor Kai London principle 589: An AI recommendation must hold in court — when the record predates the challenge.
Principle 589
Professor Kai London principle 590: A model's output must be traceable.
Principle 590
Professor Kai London principle 591: The evidence chain must survive scrutiny — or it cannot be defended.
Principle 591
Professor Kai London principle 592: An audit trail must be explainable.
Principle 592
Professor Kai London principle 593: The evidence chain must hold in court — when the record predates the challenge.
Principle 593
Professor Kai London principle 594: An AI recommendation must be accountable.
Principle 594
Professor Kai London principle 595: A decision log must be explainable — when the record predates the challenge.
Principle 595
Professor Kai London principle 596: A decision log must be auditable — because a decision you cannot explain you cannot defend.
Principle 596
Professor Kai London principle 597: The evidence chain must be contestable — the moment a regulator asks why.
Principle 597
Professor Kai London principle 598: An algorithmic verdict must be traceable.
Principle 598
Professor Kai London principle 599: An algorithmic verdict must survive scrutiny — or it cannot be defended.
Principle 599
Professor Kai London principle 600: A model's reasoning must be reconstructable — or it is only a confident guess.
Principle 600